Method and system for document classification and text information extraction

ABSTRACT

Variation in received documents types and templates used for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique. Embodiments herein provide a method and a system for document classification and text information extraction. Time efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region followed by a multi-layered neural network based architecture for enhanced classification accuracy to identify the type of document. A multistage image pre-processing approach is provided for efficient, effective, and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction, and dynamic thresholding. Further, text information extraction is applied on the pre-processed image to determine co-relation between text information blocks and associate values with the labels for attributes to be extracted to extract final text.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to Indian filed patent application No. 202021053633 filed on 9 Dec. 2020. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

The embodiments herein generally relate to text information extraction and, more particularly, to a method and system for document classification and text information extraction.

BACKGROUND

Efficient and accurate extraction of relevant text information is key factor for effectiveness of automatic text information extraction systems. Many organizations in domains such as but not limited to Banking, Finance, Insurance, Educational institutions and the like receive large volume of scanned images of documents, such as Identity Documents (IDs) and variety of forms. Quality of the scanned images varies due to factors such as illumination effect, scanning quality etc. Further, text information of interest in theses scanned images is overlaid with noise elements such as color and texture patterns, background colors and images/logos, water marks, maps, illumination etc. In practical scenarios, a user is provided with options to upload ID document or Proof of Identity (POI) document in different formats, which he/she may upload for verification and various service requests from an organization. There is no uniformity in the scanned images received as each user may upload a different type of POI. Further each region, country may have a different templates for the type of ID. Similar is the case where text information is to be extracted from scanned images of other documents such as report cards, club membership cards, marksheets, financial documents, forms and so on, wherein there may or may not be uniformity in the templates of each received scanned image.

Conventional template based matching has technical challenges in such scenarios as they need to be equipped with knowledge base of known templates. This conventional approach also mandates prior classification of documents as it is required to prior know the template to be applied. While processing, extracted document contents are compared with existing templates and text zones are compared with matched template to identify label and respective information. Further, higher the accuracy of background noise removal technique more accurate is the text extraction result. Some of the existing works attempt to address the document type variation and template variation problem but effectiveness of these approaches is majorly dependent on pre-processing of the scanned images to remove background noise. Considering the variation in received documents types with equal variation in templates for each document type, developing a generic background removal approach providing accurate background removal that addresses variation in document type, template or illumination conditions, watermarks, presence of photographs of the scanned image is a challenge. Furthermore, the text information of interest in the scanned image may not be present across entire scanned image. Thus, required are time efficient and highly accurate Region of Interest (ROI) detection techniques to rightly focus on text information of interest, which are critical to enhance the overall effectiveness of text extraction information systems.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for document classification and text information extraction is provided.

The method receives a scanned image of a document and detects a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique that locates corner coordinates of a ROI bounding box defining the ROI.

Further, the method classifies the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model.

Further, the method applies multistage pre-processing on the classified ROI to remove background noise. The multistage pre-processing comprises: a) reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; b) performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; c) obtaining a plurality of centroids, of each of the plurality of color clusters, wherein each centroid represents a unique color associated with each of the plurality of clusters; d) converting the centroids from RGB color space to Hue Saturation Value (HSV) space; e) generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask is generated based on a) the unique color associated with a centroid among the plurality of centroids and b) range of HSV color space defined around the centroid; f) applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique color of the centroid; g) identifying in each of the plurality of binary ROI images, one or more contours of interest from among the plurality of contours, wherein the one or more contours of interest are a) closed contours and b) have size above a predefined contour size; h) performing a subtraction, of the one or more of contours of interest identified for each of the plurality of binary ROI images, from the ROI in accordance to spatial positions of pixels of the one or more contours of interest identified for each of the plurality of binary ROI images, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and i) applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image.

Furthermore, the method applies a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by spatial positions defined by corner coordinates and corresponding height and width, and wherein one or more bounding boxes are clubbed based on a spatial proximity criteria.

Thereafter the method extracts text information from each of the plurality of bounding boxes by applying OCR.

Furthermore, the method determines contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type.

In another aspect, a system for document classification and text information extraction is provided. The system comprises a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to receive a scanned image of a document and detects a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique that locates corner coordinates of a ROI bounding box defining the ROI.

Further, the system classifies the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model.

Further, the system applies multistage pre-processing on the classified ROI to remove background noise. The multistage pre-processing comprises: a) reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; b) performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; c) obtaining a plurality of centroids, of each of the plurality of color clusters, wherein each centroid represents a unique color associated with each of the plurality of clusters; d) converting the centroids from RGB color space to Hue Saturation Value (HSV) space; e) generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask is generated based on a) the unique color associated with a centroid among the plurality of centroids and b) range of HSV color space defined around the centroid; f) applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique color of the centroid; g) identifying in each of the plurality of binary ROI images, one or more contours of interest from among the plurality of contours, wherein the one or more contours of interest are a) closed contours and b) have size above a predefined contour size; h) performing a subtraction, of the one or more of contours of interest identified for each of the plurality of binary ROI images, from the ROI in accordance to spatial positions of pixels of the one or more contours of interest identified for each of the plurality of binary ROI images, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and i) applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image.

Furthermore, the system applies a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by spatial positions defined by corner coordinates and corresponding height and width, and wherein one or more bounding boxes are clubbed based on a spatial proximity criteria.

Thereafter the system extracts text information from each of the plurality of bounding boxes by applying OCR.

Furthermore, the system determines contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type.

In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method for document classification and text information extraction.

The method receives a scanned image of a document and detects a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique that locates corner coordinates of a ROI bounding box defining the ROI.

Further, the method classifies the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model.

Further, the method applies multistage pre-processing on the classified ROI to remove background noise. The multistage pre-processing comprises: a) reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; b) performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; c) obtaining a plurality of centroids, of each of the plurality of color clusters, wherein each centroid represents a unique color associated with each of the plurality of clusters; d) converting the centroids from RGB color space to Hue Saturation Value (HSV) space; e) generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask is generated based on a) the unique color associated with a centroid among the plurality of centroids and b) range of HSV color space defined around the centroid; f) applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique color of the centroid; g) identifying in each of the plurality of binary ROI images, one or more contours of interest from among the plurality of contours, wherein the one or more contours of interest are a) closed contours and b) have size above a predefined contour size; h) performing a subtraction, of the one or more of contours of interest identified for each of the plurality of binary ROI images, from the ROI in accordance to spatial positions of pixels of the one or more contours of interest identified for each of the plurality of binary ROI images, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and i) applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image.

Furthermore, the method applies a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by spatial positions defined by corner coordinates and corresponding height and width, and wherein one or more bounding boxes are clubbed based on a spatial proximity criteria.

Thereafter the method extracts text information from each of the plurality of bounding boxes by applying OCR.

Furthermore, the method determines contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:

FIG. 1 is a functional block diagram of a system for document classification and text information extraction, in accordance with some embodiments of the present disclosure.

FIGS. 2A through 2C depict a flow diagram illustrating a method for document classification and text information extraction, using the system of FIG. 1 , in accordance with some embodiments of the present disclosure.

FIG. 3A through 3M is a use case example depicting document classification and text information extraction using the system of FIG. 1 , in accordance with some embodiments of the present disclosure.

FIG. 4 illustrates an example implementation of a spatial proximity criteria that combines one or more text bounding boxes into a single text region, in accordance with some embodiments of the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.

Variation in type or class of documents received as scanned/uploaded images along with equal variation in templates for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique.

Embodiments herein provide a method and a system for document classification and text information extraction by providing a generic background noise removal technique. Method comprises first detecting an Region of Interest in a received scanned image, wherein a computation efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region, followed by a multi-layered neural network based architecture for accurate classification of the document type or class. A multistage image pre-processing approach is provided for efficient, effective and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction and dynamic thresholding. Further, text information extraction is applied on the pre-processed image to determine co-relation between text information blocks and associate values with the labels for attributes to be extracted, to extract final text.

The term ‘document’ used herein refers to any documents having text information overlaid on background noise elements such as color marks and texture patterns, background images, logos, maps, watermarks etc. Some examples of these documents are proof of identity (POI) documents like passports, national identification cards, driver license etc.; other documents having background noise such as club membership cards, student mark sheets, financial statements etc.

Method disclosed herein provides a generic text information extraction solution that is document type or template agnostic and utilizes multistage pre-processing to eliminate background noise' the multistage pre-processing further includes performing dynamic thresholding prior to processing the image using OCR for text extraction. The threshold value is derived from a histogram generated for the current scanned image under consideration. Thus, the thresholding approach utilized is specific to the image being processed resulting in image specific threshold value, effectively enhancing the thresholding effect providing more clarity and visibility of text content in the scanned image. The enhanced text visibility improves text presence detection for marking text bounding boxes and further improves accuracy in text extraction using OCR.

Referring now to the drawings, and more particularly to FIGS. 1 through 4 , where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

FIG. 1 is a functional block diagram of a system 100 for document classification and text information extraction, in accordance with some embodiments of the present disclosure.

In an embodiment, the system 100, includes a processor(s) 104, communication interface device(s), alternatively referred as input/output (I/O) interface(s) 106, and one or more data storage devices or a memory 102 operatively coupled to the processor(s) 104. The system 100 with one or more hardware processors is configured to execute functions of one or more functional blocks of the system 100.

Referring to the components of system 100, in an embodiment, the processor(s) 104, can be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 104 are configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, and the like.

The I/O interface(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite to receive scanned images and communicate with external devices for sharing the results, or additional processing support on the cloud servers. The memory 102 further includes a Deep Learning based multi-layered Neural Network (NN) classification model (not shown), which is executed via the one or more hardware processors to classify the received scanned images of a document into a class (document type)_among a plurality of predefined classes (document types). The memory 102 also includes configuration rules, text engine and other modules for executing the steps of the method. In an embodiment, the I/O interface (s) 106 can include one or more ports for connecting to a number of external devices or to another server or devices.

The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

Further, the memory 102 may include the database 108 comprising the received scanned images, pre-processed images at first and second level of pre-processing, extracted text and so on. Thus, the memory 102 may comprise information pertaining to input(s)/output(s) of each step performed by the processor(s) 104 of the device 100 and methods of the present disclosure. In an embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I/O interface 106. Functions of the components of the system 100 are explained in conjunction with flow diagram depicted in FIGS. 2A through 2C, the use case examples depicted in FIGS. 3A through 3M and context based correlation of extracted text explained in conjunction with FIG. 4 .

FIG. 2 is a flow diagrams illustrating a method 200 for document classification and text information extraction, using the system 100 of FIG. 1 , in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method 200 by the processor(s) or one or more hardware processors 104. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIGS. 2A through 2C. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.

The method 200 is explained in conjunction with FIG. 3A through 3M which is a use case example depicting document classification and text information extraction using the system of FIG. 1 , in accordance with some embodiments of the present disclosure. FIG. 3A depicts a sample driving license used to explain the method disclosed.

Referring to the steps of the method 200, at step 202, the one or more hardware processors 104 are configured to receive a scanned image of a document as input, as depicted in FIG. 3A. The scanned image as can be seen in FIG. 3A may be a Proof of Identity (POI) document such as a driving license, wherein the image of document of interest is captured as a foreground object placed on a background. It is necessary to rightly focus on region of the image that maps to the actual document and eliminate the irrelevant background.

At step 204 the one or more hardware processors 104 are configured to detect a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique that locates corner coordinates of a ROI bounding box defining the ROI. The ROI detection is explained in conjunction with FIG. 3B to 3F. As understood, the input scanned images may have varying background with variation in color, texture, illumination and is not fixed across scanned images received. Thus, conventional computer vision techniques face challenges is accurate ROI detection. The steps for text engine based-ROI detection disclosed by the method include:

-   -   a) Detect the text in the input image by using a text detector         engine as depicted in FIG. 3B and marking text regions in the         scanned image, identified by corner coordinates of rectangular         bounding boxes t1 to tn, as depicted in FIG. 3C.     -   b) Determine a rectangular bounding box as depicted in FIG. 3D         enclosing all the text regions t1 to tn and generate an output         image set for the entire text regions spread across the scanned         image, as depicted in FIG. 3E. Determine the corner coordinates         as depicted.     -   c) Crop the scanned image in accordance with the rectangular         bounding box of FIG. 3D defined by corner coordinates as         depicted in FIG. 3E.

Once the ROI is rightly identified at step 204, then at step 206 of the method 200, the one or more hardware processors 104 are configured to classify the ROI into a document type among a plurality of document types using the Deep Learning based multi-layered Neural Network (NN) classification model. The document types can include a passport, a driving license, a unique identification Document (UID), a membership cards and the like. The architecture of the multi-layered Neural Network (NN) classification model used for document classification is provided in table 1 below. The model is trainable, and the training steps include:

-   -   a) Generate sample images as training data.     -   b) Perform data pre-processing, data augmentation and data         annotation to train the multi-layered NN based classification         model such as CNN model. However, the method disclosed provides         configurable model architecture, wherein complexity of the model         architecture can be increased or decreases based on training         data.

TABLE 1 Layer (type) Output Shape Param # conv2d_1 (Conv2D) (None, 125, 125, 64) 1792 conv2d_2 (Conv2D) (None, 125, 125, 64) 36928 max_pooling2d_1 (MaxPooling2 (None, 62 62, 64) 0 dropout_1 (Dropout) (None, 62, 62, 64) 0 batch_normalization_1 (Batch (None, 62, 62, 64) 256 conv2d_3 (Conv2D) (None, 62, 62, 128) 73856 max_pooling2d_2 (MaxPooling2 (None, 31, 31, 128) 0 dropout_2 (Dropout) (None, 31, 31, 128) 0 batch_normalization_2 (Batch (None, 31, 31, 128) 512 conv2d_4 (Conv2D) (None, 31, 31, 128) 147584 max_pooling2d_3 (MaxPooling2 (None, 15, 15, 128) 0 dropout_3 (Dropout) (None, 15, 15, 128) 0 flatten_1 (Flatten) (None, 28800) 0 dense_1 (Dense) (None, 256) 7373056 dropout_4 (Dropout) (None, 256) 0 dense_2 (Dense) (None, 3) 771 Total params: 7,634,755 Trainable params: 7,634,371 Non-trainable params: 384

Upon classification and cropping of the scanned document in accordance with he detected ROI, at step 208 the one or more hardware processors 104 are configured to apply multistage pre-processing on the classified ROI to remove background noise. The multistage pre-processing is explained in conjunction with FIGS. 3G through 3K. The multistage pre-processing comprises:

-   -   a) Read (208 a) the ROI into a Red Green Blue (RGB) color space         and flattening the ROI (as depicted in FIG. 3G), wherein the 2D         image channels are converted to vector space.     -   b) Perform (208 b) unsupervised clustering by applying K-means         clustering on a plurality of pixels of the ROI in the RGB color         space to generate a plurality of color clusters (as depicted in         FIG. 3G). This step enables to identify number of significant         color present in the ROI and number of pixels or pixel density         per identified color clusters. Thus, each cluster represents a         color range with pixels, whose RGB values closely match with         other pixels of the same cluster. Number of clusters are preset         to 12, hence value of K=12. This value of K is determined         heuristically. The logic behind deriving the value of K is         explained below.         -   In HSV color space, H value ranges from 0-179, i.e. total             180 discrete values. Within this range each of the three             primary colors are represented by 60 discrete values. There             are two reasons for selecting K=12.             -   1) Using K=12, we are detecting 12 different color                 clusters. So, for each primary color 4 variants of that                 color are used in an image for all the colors, which is                 a worst case scenario. If image has fewer colors, then                 we can identify more variants of the same color.             -   2) The second reason is to use fewer colors because it                 is required to identify color patches which consist of                 pixels which are similar in color, but not necessarily                 with exact same intensity value. If larger value of K is                 used, then the clustering will be more fine grained and                 there exists possibility of detecting multiple patches                 instead of single patch because of minor color                 variations.         -   Further, once color clusters are formed pixel density or             frequency information per cluster can be obtained, which is             quantitative indication of density of each color pixels in             the image. This information can be used to selectively             eliminate the non-significant colors, as required. Further,             pixel density information per color can be used to derive             various interpretations, for example, the color with highest             pixel density is most likely to represent background etc.     -   c) Obtain (208 c) a plurality of centroids, of each of the         plurality of color clusters, wherein each centroid represents a         unique mean color associated with each of the plurality of         clusters.     -   d) Since the further processing of the ROI is focused on color         of the image pixels, rather than RGB color space, the method         disclosed utilizes HSV color space since H (Hue) provides the         true color and eliminates effect of illumination on present in         RGB values. Thus, step includes converting (208 d) the centroids         from RGB color space to Hue Saturation Value (HSV) space.     -   e) Generate (208 e) a plurality of color masks corresponding to         the plurality of clusters, wherein each color mask is generated         based on a) the unique color associated with a centroid among         the plurality of centroids and b) range of HSV color space         defined around the centroid (FIG. 3H). Each of the color mask is         generated such that it sets the pixels of the centroid along         pixels having HSV values withing the HSV range defined around         the HSV of the centroid to say 1 (white) while rest of the         pixels to 0 pixel intensity (black) to generate a binary image.     -   f) Apply (208 f) each of the plurality of color masks to the ROI         to obtain a plurality of binary ROI images, wherein each of the         plurality of binary ROI image comprises one or more contours         indicating spatial locations of one or more pixels among the         plurality of pixels in the ROI that belong to the unique color         of the centroid (FIG. 3I). For example a map in the ROI (say in         color red), the embedded chip on the driving license (say         golden/yellow color) and the face in the photographs (say the         skin color) are set to white while rest of the color pixels are         set to black to generate the binary image per color mask. From         the binary images, plurality of contours associated with white         regions in the binary image are identified.     -   g) Identify (208 g), in each of the plurality of binary ROI         images, one or more contours of interest from among the         plurality of contours, wherein the one or more contours of         interest are i) closed contours and ii) have size above a         predefined contour size (FIG. 3I). Thus, the resultant image         post masking may have open contours, closed contours, and closed         but very small contours. It is observed from heuristic         approaches that background noise is present in closed contours,         however too small closed contours not necessarily include the         background noise. Thus, a contour selection criteria is set by         the method as mentioned above. If a contour is too small (below         the threshold value), then it is observed that it does not form         a significant background noise that can obstruct our OCR         accuracy or it may be part of the useful information that needs         to be preserved and not eliminated as background noise. Hence         the contours below a certain size are not relevant as background         and are ignored.         -   Such small contours are generated due to the nature of the             image and its content.     -   h) Perform (208 h) a subtraction of the one or more contours of         interest identified for each of the plurality of binary ROI         images, from the ROI in accordance to spatial positions of         pixels of the one or more contours of interest identified for         each of the plurality of binary ROI images. The subtraction         eliminates the background noise while retaining information of         interest to generate a first level pre-processed image.     -   i) In text extraction, thresholding is required to be applied on         an image before proceeding with OCR. However, if thresholding is         applied directly without first level pre-processing the         resultant image carries background noise that hampers OCR         output. Existing thresholding techniques used by works in         literature have limitations, and resultant thresholded image         using conventional thresholding on images that have noise         elements such as water marks, leaves behind hazy areas or dark         pixel marks, when used for text extraction, results in poor         extraction quality of text extraction.         -   Existing thresholding techniques cause loss of text             information, due to small text area compared to the             background & inherent noise condition in document images.             Further, local neighborhood based approach calculates             different thresholding values for different parts and             inherently amplifies the noise in some cases.         -   However, the dynamic thresholding disclosed herein, works on             the histogram generated from the image after the background             noises are removed (histogram generated for the first             pre-processed image). The peaks present in the histogram             identify the foreground and background, and it is identified             experimentally that mean of the peaks (left and right peak             values) is the optimal spot of the global threshold value             for the image. Hence it performs much better for the text             extraction objective described herein, and resultant image             is more suitable for OCR.         -   Thus, method disclosed enables applying (208 i) thresholding             using a dynamically derived threshold value on the first             level pre-processed image to obtain a second level             pre-processed image. The dynamic thresholding as depicted in             FIGS. 3J and 3K and deriving the threshold value dynamically             comprises step of:             -   1. Calculating the histogram of the first level                 pre-processed image.             -   2. Determining average pixel intensity of the first                 level pre-processed image and a left most and a right                 most peak value in the histogram.             -   3. Calculating the threshold value by averaging the left                 most and the right most peak values. As explained above,                 the peaks present in the histogram identify the                 foreground and background, and it is identified                 experimentally that mean of the peaks (left and right)                 peak values) is the optimal spot of the global threshold                 value for the image.

Referring back to the step 210 of the method 200, the one or more hardware processors 104 are configured to apply text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information (text regions) in the second level pre-processed image as depicted in FIG. 3L. The plurality of bounding boxes are identified by spatial positions defined by corner coordinates and corresponding height and width. However, in first round of text region identification, spatially close words that mostly relate to single information field may still be identified by separate text bounding boxes. For example,

INDIAN UNION DRIVING LINCENSE

Thus, one or more bounding boxes are clubbed based on a spatial proximity criteria. The spatial proximity criteria analyses proximity or overlaps among spatially relatable text bounding boxes to club one or more bounding boxes of text regions to associate relatable information into a single bounding, as below. The final bounding boxes post clubbing are depicted in FIG. the sample driving license.

INDIAN UNION DRIVING LINCENSE

Thus, the entire clubbed text region is considered for interpreting the text information within. For the example above, it is understood to the clubbed text region provides name of issuing authority for the driving license.

FIG. 4 illustrates an example implementation of a spatial proximity criteria that combines one or more text bounding boxes into a single text region, in accordance with some embodiments of the present disclosure.

The spatial proximity criteria based analysis includes receiving identified text regions (bounded boxes) as input. Further, based on spatial information of identified text region, establishes contextual relationship between group of identified text regions For example—Name “John Doe” has 2 words which are horizontally aligned on identification card and “John” and “Doe” together has to be grouped together based on their spatial location and inferred together as name of person on identification card.

Pseudocode 1 for spatial proximity criteria (context based correlation) is provided below:

Pseudo code 1-Horizontal Neighbor finding algorithm: Input : file_name, text_box_lists, bounding_height_limit): Output: horizontal grouped text box list Algorithm: Loop text_box in text_box_list  Loop grouped_text_box in grouped text box list   x1_min, y1_min, x1_max, h1_max = box_param in text_box   x2_min, y2_min, x2_max, h2_max = box_param in grouped_text_box   if (y2_min −y1_min) < bounding_height_limit    Push minimum(x1_min, x2_min) to grouped_text_box    Push minimum(y1_min, y2_min) to grouped_text_box    Push maximum(x1_max, x2_max) to grouped_text_box    Push maximum(y1_max, y2_max) to grouped_text_box   Else    add text_box to grouped_text_box  End loop End loop

-   -   At step 212 of the method 200, the one or more hardware         processors are configured to extract text information from each         of the plurality of bounding boxes by applying OCR.

At step 214 of the method 200, the one or more hardware processors 104 are configured to determine contextual relationship among the extracted text information and refine the extracted text information based on configuration rules for the document type, As depicted in FIG. 3M

The configuration rules comprise precompiled knowledge base that is referred to identify undesired text from the extracted text information, wherein the undesired text is discarded from the extracted text information, and wherein the precompiled knowledge base is composition of KEYWORDS, and VALUE_CATEGORY.

For example:

1) KEYWORDS: If we consider any Indian Driving License image, the Keyword “Indian Union Driving License” is common across all driving licenses. This keyword doesn't provide any information about the card holder, so, it should be treated as noise.

2) VALUE_CATEGORY: The possible value for the field is Text, Number, Alphanumeric, date. If we consider any Indian Driving License image, there are multiple information like card holder name, dob, gender present in the image. If the dob needs to be extracted among all the information, the VALUE_CATEGORY field value for dob will be date, which helps to consider only number and delimiter from the extracted text. 3) String similarity is leveraged here to find out the nearest possible match for desired label. Once identified, the location of matched label deduced using String processing method. Predefined spatial associations are used to fetch the desired value of interest. Further validation has been applied on the value of interest.

Pseudo code 2 for Extract values: Input: Text_box: {x-min, y-min, x-max, y-max, text_box_ocr_extracted_value, text_box_ocr_extracted_confidence} x-min: x-min-coordinate, y-min: y-min -coordinate, x-max: x-max -coordinate, y-max: y-max -coordinate Text_box_list = List of Text Box training_data_list : list of {label_name : list of extraction rule} Output: extracted_items_list : List of extracted items i.e. [label_name: extracted item ] Algorithm:  for text_box in text_box_list:    Remove the text which is matching with the UNIQUE_KEYWORDS present in the training_data     for training_data in traning_data_list:      text := remove junk data from text      prepare the extracted value as per the VALUE_CATEGORY like date   or, alphabet or, number by using regex pattern like ‘dd/mm/yyyy’      If the IS_LABEL_PRESENT value is NO       text := Extract the value by using regex pattern      ELSE If the IS_LABEL_PRESENT value is YES       If the ENTITY_VALUE_ASSOCIATION_TYPE = Right        text := Checks the text with the label value and accept the text value after the label       ELSE If the ENTITY_VALUE_ASSOCIATION_TYPE = BELOW        text := Checks the text with the label value and consider the text value present after the text box      validate text and if it is valid :       set extracted_item as {label_name : text }       push extracted_item in extracted_items_list     End loop    End loop

At step 216, the method 200 further comprises providing confidence score to enable analysis on quality of text extraction. Multiple confidence scores and an aggregate confidence scores are computed to assess quality of different extraction steps of the method. The confidence score computation includes:

-   -   a) generating confidence scores for each of the classified         document type, detected text boxes, the extracted text         information and a name validator     -   b) assigning predefined weights to each of the confidence scores     -   c) aggregating the weighted confidence score to compute a         cumulative confidence score for the extracted text information.     -   Steps for confidence score calculation approach include multiple         functional components for individual confidence score         calculation. The components include a text detection component,         a text extraction component, a string similarity component, a         document classification component, and a name validator         component     -   a) Text Detection component:—It is a deep learning based model,         which detects the text boundary boxes along with the text         detection confidence score for every predicted text region in an         input image. The confidence score is passed to the Confidence         score calculator Component.         C _(DETECT)=Σ_(i=1) ^(n) ·CW ^((i)) _(DETECT) /n  (1)         -   Where, C_(DETECT)=Confidence score obtained from the Text             Detection Component for the detection. The value of COCR may             be between 0 and 1.0<=C_(DETECT)<=1         -   CW^((i)) _(DETECT)=Confidence score of i^(th) word contained             in the image given by Text Detection Component         -   n=total number of words in the image     -   b) Text Extraction Component:—It is based on OCR engine. It         gives output as the text and corresponding OCR confidence score         for the given image. The confidence score is passed to the         Confidence score calculator Component.         C _(OCR)=Σ_(i=1) ^(n) ·CW ^((i)) _(OCR) /n  (2)         -   C_(OCR)=Confidence score obtained from the OCR technique for             the label value of given label. The value of C_(OCR) may be             between 0 and 1. 0<=C_(OCR)<=1         -   CW^((i)) _(OCR)=Confidence score of i-^(th) word contained             within the value given by OCR tool         -   n=total number of words in the value string     -   c) Document Classification Component: It is CNN based deep         learning model used to detect the document type, along with that         the confidence score of detecting document type.         -   It is denoted as CClassification     -   d) String similarity Component: An inference Engine workflow is         based on the String Similarity Component which helps to give the         String similarity confidence score. It is based on Levenshtein         distance, for measuring the difference between two sequences. It         is denoted as C_(SIMILARITY). The Levenshtein distance between         two strings is given as:

$\begin{matrix} {{{lev}\left( {a,b} \right)} = \left\{ \begin{matrix} {a} & {{{{if}\mspace{14mu}{b}} = 0},} \\ {b} & {{{{if}\mspace{14mu}{a}} = 0},} \\ {{lev}\left( {{{tail}(a)},{{tail}(b)}} \right)} & {{{if}\mspace{14mu}{a\lbrack 0\rbrack}} = {b\lbrack 0\rbrack}} \\ {1 + {\min\left\{ \begin{matrix} {{lev}\left( {{{tail}(a)},b} \right)} \\ {{lev}\left( {a,{{tail}(b)}} \right)} \\ {{lev}\left( {{{tail}(a)},{{tail}(b)}} \right.} \end{matrix} \right.}} & {{otherwise}.} \end{matrix} \right.} & (3) \end{matrix}$

-   -   e) Name Validator Component: The component is used to validate         the OCR extracted text is a valid name or, not. The         encoder-decoder model generates a prediction score for the next         character given the previous characters. So, a character that         has a high probability of occurrence, in the sequence, will have         a high prediction score. It is denoted as         C _(VALIDATOR)=Σ_(i=1) ^(n) ·CW ^((i)) _(VALIDATOR) /n  (4)         -   C_(VALIDATOR)=Confidence score obtained from the Name             Validator Component for the label value. The value of             C_(VALIDATOR) may be between 0 and 1. 0<=C_(VALIDATOR)<=1         -   CW^((i)) _(VALIDATOR)=a prediction score for the next             character given the previous characters of the i^(th)             character         -   n=total number of characters in the value string     -   f) Aggregate of the prediction scores for each character is         taken and the value is normalized with respect to the length of         the sequence. Final confidence score is calculated by         normalising the confidence score values from above mentioned         components and taking the weighted sum of these values. The         weight coefficients of the weighted sum formula are calibrated         on the basis of which component should be given higher weightage         than other components.         C _(AGG)=(a*C _(OCR) +b*C _(DETECTION) +c*C _(CLASSIFICATION)         +d*C _(SIMILARITY) +e*C _(VALIDATOR))/(a+b+c+d+e)  (5)         -   Where, C_(AGG)=Final Aggregate Confidence score for value of             label. This value will be between 0 and 1.         -   0<=C_(AGG)<=1         -   a, b, c, d, e=These are coefficient weights assigned to all             individual confidence scores based on what weightage needs             to be given on which confidence score. Usually OCR and             Detection are given high weightage.             A pseudo code for confidence score calculation is provided             below:

Confidence Score Consolidator:

Find the document classification score:

doc_classification_conf←predicted by classification model in which entity lies.

iterate each entity_name entity_name list:

text_region_conf_score←prediction confidence score by region detector component

calculate entity_value;

value_word_list←break into multi words

iterate value word in value_word_list:

-   -   aggregate_ocr_conf_score:=ocr confidence score of each word

end loop

text_extraction_conf_score←aggregate_ocr_conf_score

string_correction_based_conf_score←confidence score obtained after similarity/post-match

normalize the values of doc_classification_conf, text_region_conf_score, text_extraction_conf_score, string_correction_based_conf_score

cumulativate_confidence:=A*doc_classification_conf+B*text_region_conf_score+C*text_extraction_conf_score+D*string_correction_based_conf_score

return cumulative_confidence;

end loop

The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means, and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims. 

What is claimed is:
 1. A method for classification of a document and text information extraction of the document, the method comprising: receiving, via one or more hardware processors, a scanned image of the document; detecting, via the one or more hardware processors, a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique, wherein the text detection engine based-ROI technique locates one or more corner coordinates of the ROI bounding box defining the ROI; classifying, via the one or more hardware processors, the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model, wherein training of the trainable Deep Learning based multi-layered NN classification model comprises: generating sample images as training data; and performing pre-processing of the training data, augmentation of the training data, and data annotation of the training data to train the trainable Deep Learning based multi-layered NN classification model, wherein the trainable Deep Learning based multi-layered NN classification model includes an architecture that is configurable based on requirements, and a complexity of the architecture of the trainable Deep Learning based multi-layered NN classification model is varied based on the training data; applying, via the one or more hardware processors, multistage pre-processing on the classified ROI to remove a background noise, wherein the multistage pre-processing comprises: reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; obtaining a plurality of centroids from each of the plurality of color clusters, wherein each centroid of the plurality of centroids represents a unique mean color associated with each of the plurality of clusters; converting the plurality of centroids from the RGB color space to a Hue Saturation Value (HSV) space; generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask of the plurality of color masks is generated based on a) the unique mean color associated with a centroid among the plurality of centroids and b) a range of the HSV color space defined around the centroid, wherein each of the plurality of color masks is generated such that each color mask sets: a plurality first specific of pixels of the plurality of pixels of the centroid having HSV values within the HSV range to a first value, and a plurality of second specific pixels of the plurality of pixels to a second value; applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique mean color of the centroid; identifying in each of the plurality of binary ROI images, one or more contours of interest from the plurality of contours, wherein the identified one or more contours of interest are a) closed contours and b) the identified one or more contours of interest has a size above a predefined contour size; performing subtraction of the one or more identified contours of interest from the ROI in accordance to spatial positions of the plurality of pixels of the identified one or more contours of interest, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image, wherein the threshold value is derived dynamically by: calculating the histogram of the first level pre-processed image; determining average pixel intensity of the first level pre-processed image and a left most value and a right most peak value in the histogram; and calculating the threshold value by averaging the left most and the right most peak values; applying, via the one or more hardware processors, a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by one or more spatial positions defined by the one or more corner coordinates and a corresponding height and a corresponding width, one or more bounding boxes of the plurality of bounding boxes are clubbed based on a spatial proximity criteria, the spatial proximity criteria analyzes a proximity among the one or more bounding boxes which has spatially relatable text to club the one or more bounding boxes, and each of the one or more bounding boxes are clubbed to form relatable information into a single bounding; extracting, via the one or more hardware processors, text information from each of the plurality of bounding boxes by applying an Optical Character Recognition (OCR); and determining, via the one or more hardware processors, contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type.
 2. The method of claim 1, wherein the configuration rules comprise a precompiled knowledge base that is referred to identify undesired text from the extracted text information, wherein the undesired text is discarded from the extracted text information, and wherein the precompiled knowledge base is composition of KEYWORDS and VALUE_CATEGORY.
 3. The method of claim 1, further comprises correcting, via the one or more hardware processors, name values from the extracted text information using an encoder-decoder model that utilizes Long short-term memory (LSTM) based Recurrent neural networks (RNN) architecture.
 4. The method of claim 1, further comprises: generating confidence scores for each of the classified document type, the detected text boxes, the extracted text information, and an output of a name validator; assigning predefined weights to each of the generated confidence scores; and aggregating the weighted confidence scores to compute a cumulative confidence score for the extracted text information.
 5. A system for classification of a document and text information extraction of the document, the system comprising: a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to: receive a scanned image of the document; detect a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique, wherein the text detection engine based-ROI technique locates one or more corner coordinates of the ROI bounding box defining the ROI; classify the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model, wherein training of the trainable Deep Learning based multi-layered NN classification model comprises: generating sample images as training data; and performing pre-processing of the training data, augmentation of the training data, and data annotation of the training data to train the trainable Deep Learning based multi-layered NN classification model, wherein the trainable Deep Learning based multi-layered NN classification model includes an architecture that is configurable based on requirements, and a complexity of the architecture of the trainable Deep Learning based multi-layered NN classification model is varied based on the training data; apply multistage pre-processing on the classified ROI to remove a background noise, wherein the multistage pre-processing comprises: reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; obtaining a plurality of centroids from each of the plurality of color clusters, wherein each centroid of the plurality of centroids represents a unique mean color associated with each of the plurality of clusters; converting the plurality of centroids from the RGB color space to a Hue Saturation Value (HSV) space; generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask of the plurality of color masks is generated based on a) the unique mean color associated with a centroid among the plurality of centroids and b) a range of the HSV color space defined around the centroid, wherein each of the plurality of color masks is generated such that each color mask sets: a plurality first specific of pixels of the plurality of pixels of the centroid having HSV values within the HSV range to a first value, and a plurality of second specific pixels of the plurality of pixels to a second value; applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique mean color of the centroid; identifying in each of the plurality of binary ROI images, one or more contours of interest from the plurality of contours, wherein the identified one or more contours of interest are a) closed contours and b) the identified one or more contours of interest has a size above a predefined contour size; performing subtraction of the one or more identified contours of interest from the ROI in accordance to spatial positions of the plurality of pixels of the one or more identified contours of interest, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image, wherein the threshold value is derived dynamically by: calculating the histogram of the first level pre processed image; determining average pixel intensity of the first level pre-processed image and a left most peak value and a right most peak value in the histogram; and calculating the threshold value by averaging the left most peak value and the right most peak value; apply a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by one or more spatial positions defined by the one or more corner coordinates and a corresponding height and a corresponding width, one or more bounding boxes of the plurality of bounding boxes are clubbed based on a spatial proximity criteria, the spatial proximity criteria analyzes a proximity among the one or more bounding boxes which has spatially relatable text to club the one or more bounding boxes, and each of the one or more bounding boxes are clubbed to form relatable information into a single bounding; extract text information from each of the plurality of bounding boxes by applying an Optical Character Recognition (OCR); and determine contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type.
 6. The system of claim 5, wherein the configuration rules comprise a precompiled knowledge base that is referred to identify undesired text from the extracted text information, wherein the undesired text is discarded from the extracted text information, and wherein the precompiled knowledge base is composition of KEYWORDS and VALUE_CATEGORY.
 7. The system of claim 5, wherein the one or more hardware processors are further configured to correct Name values from the extracted data using an encoder-decoder model that utilizes Long short-term memory (LSTM) based Recurrent neural networks (RNN) architecture.
 8. The system of claim 5, wherein the one or more hardware processors are configured to: generate confidence scores for each of the classified document type, detected text boxes, the extracted text information, and an output of a name validator; assign predefined weights to each of the generated confidence scores; and aggregating the weighted confidences score to compute a cumulative confidence score for the extracted text information.
 9. One or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes: receiving a scanned image of a document; detecting a Region of Interest (ROI) in the scanned image by marking a ROI bounding box on the scanned image using a text detection engine based-ROI technique, wherein the text detection engine based-ROI technique locates one or more corner coordinates of the ROI bounding box defining the ROI; classifying the ROI into a document type among a plurality of document types using a trainable Deep Learning based multi-layered Neural Network (NN) classification model, wherein training of the trainable Deep Learning based multi-layered NN classification model comprises: generating sample images as training data; and performing pre-processing of the training data, augmentation of the training data, and data annotation of the training data to train the trainable Deep Learning based multi-layered NN classification model, wherein the trainable Deep Learning based multi-layered NN classification model includes an architecture that is configurable based on requirements, and a complexity of the architecture of the trainable Deep Learning based multi-layered NN classification model is varied based on the training data; applying multistage pre-processing on the classified ROI to remove a background noise, wherein the multistage pre-processing comprises: reading the ROI into a Red Green Blue (RGB) color space and flattening the ROI; performing unsupervised clustering by applying K-means clustering on a plurality of pixels of the ROI in the RGB color space to generate a plurality of color clusters; obtaining a plurality of centroids from each of the plurality of color clusters, wherein each centroid of the plurality of centroids represents a unique mean color associated with each of the plurality of clusters; converting the plurality of centroids from the RGB color space to a Hue Saturation Value (HSV) space; generating a plurality of color masks corresponding to the plurality of clusters, wherein each color mask of the plurality of color masks is generated based on a) the unique mean color associated with a centroid among the plurality of centroids and b) a range of the HSV color space defined around the centroid, wherein each of the plurality of color masks is generated such that each color mask sets: a plurality first specific of pixels of the plurality of pixels of the centroid having HSV values within the HSV range to a first value, and a plurality of second specific pixels of the plurality of pixels to a second value; applying each of the plurality of color masks to the ROI to obtain a plurality of binary ROI images, wherein each of the plurality of binary ROI image comprises one or more contours indicating spatial locations of one or more pixels among the plurality of pixels in the ROI that belong to the unique mean color of the centroid; identifying in each of the plurality of binary ROI images, one or more contours of interest from the plurality of contours, wherein the identified one or more contours of interest are a) closed contours and b) the identified one or more contours of interest has a size above a predefined contour size; performing subtraction of the one or more identified contours of interest from the ROI in accordance to spatial positions of the plurality of pixels of the identified one or more contours of interest, wherein the subtraction eliminates the background noise while retaining information of interest to generate a first level pre-processed image; and applying thresholding on the first level pre-processed image to obtain a second level pre-processed image using a threshold value derived dynamically from a histogram of the first level pre-processed image, wherein the threshold value is derived dynamically by: calculating the histogram of the first level pre processed image; determining average pixel intensity of the first level pre-processed image and a left most value and a right most peak value in the histogram; and calculating the threshold value by averaging the left most and the right most peak values; applying a text detection technique on the second level pre-processed image to mark a plurality of bounding boxes around text information in the second level pre-processed image, wherein each of the plurality of bounding boxes are identified by one or more spatial positions defined by the or more corner coordinates and a corresponding height and a corresponding width, one or more bounding boxes of the plurality of bounding boxes are clubbed based on a spatial proximity criteria, the spatial proximity criteria analyzes a proximity among the one or more bounding boxes which has spatially relatable text to club the one or more bounding boxes, and each of the one or more bounding boxes are clubbed to form relatable information into a single bounding; extracting text information from each of the plurality of bounding boxes by applying an Optical Character Recognition (OCR); and determining contextual relationship among the extracted text information and refining the extracted text information based on configuration rules for the document type. 